Crossing The Gap: A Deep Dive into Zero-Shot Sim-to-Real Transfer for Dynamics

August 15, 2020 ยท Entered Twilight ยท ๐Ÿ› IEEE/RJS International Conference on Intelligent RObots and Systems

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Repo contents: LICENSE, ReadMe.md, requirements.txt, robosuite-extra, sim2real-calibration-characterisation, sim2real-policies

Authors Eugene Valassakis, Zihan Ding, Edward Johns arXiv ID 2008.06686 Category cs.RO: Robotics Cross-listed cs.LG Citations 52 Venue IEEE/RJS International Conference on Intelligent RObots and Systems Repository https://github.com/eugval/sim2real_dynamics_simulation โญ 26 Last Checked 29 days ago
Abstract
Zero-shot sim-to-real transfer of tasks with complex dynamics is a highly challenging and unsolved problem. A number of solutions have been proposed in recent years, but we have found that many works do not present a thorough evaluation in the real world, or underplay the significant engineering effort and task-specific fine tuning that is required to achieve the published results. In this paper, we dive deeper into the sim-to-real transfer challenge, investigate why this is such a difficult problem, and present objective evaluations of a number of transfer methods across a range of real-world tasks. Surprisingly, we found that a method which simply injects random forces into the simulation performs just as well as more complex methods, such as those which randomise the simulator's dynamics parameters, or adapt a policy online using recurrent network architectures.
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